Ian En-Hsu Yen

50 papers A* 18A 5B 1Journal 17Unranked 8
YearRankTypeTitle / Venue / Authors
2025 A* conf
MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding.
ICLR
Ranajoy Sadhukhan, Jian Chen, Zhuoming Chen, Vashisth Tiwari, Ruihang Lai, Jinyuan Shi, Ian En-Hsu Yen, Avner May, Tianqi Chen, Beidi Chen
2025 J jnl
SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs.
CoRR
Ruixuan Huang, Hao Zeng, Hantao Huang, Jinyuan Shi, Minghui Yu, Ian En-Hsu Yen, Shuai Wang
2024 J jnl
MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding.
CoRR
Jian Chen, Vashisth Tiwari, Ranajoy Sadhukhan, Zhuoming Chen, Jinyuan Shi, Ian En-Hsu Yen, Beidi Chen
2023 J jnl
FP8-BERT: Post-Training Quantization for Transformer.
CoRR
Jianwei Li, Tianchi Zhang, Ian En-Hsu Yen, Dongkuan Xu
2022 J jnl
S4: a High-sparsity, High-performance AI Accelerator.
CoRR
Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu
2022 — conf
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.
ACL (1)
Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding
2022 B conf
Towards ℓ1 Regularization for Deep Neural Networks: Model Sparsity Versus Task Difficulty.
DSAA
Ta-Chun Shen, Chun-Pai Yang, Ian En-Hsu Yen, Shou-De Lin
2021 — conf
Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm.
NAACL-HLT
Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao
2021 J jnl
Rethinking Network Pruning - under the Pre-train and Fine-tune Paradigm.
CoRR
Dongkuan Xu, Ian En-Hsu Yen, Jinxi Zhao, Zhibin Xiao
2021 J jnl
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.
CoRR
Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding
2020 A* conf
Minimizing FLOPs to Learn Efficient Sparse Representations.
ICLR
Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2020 J jnl
Minimizing FLOPs to Learn Efficient Sparse Representations.
CoRR
Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
2019 A* conf
Efficient Global String Kernel with Random Features: Beyond Counting Substructures.
KDD
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal
2019 J jnl
Efficient Global String Kernel with Random Features: Beyond Counting Substructures.
CoRR
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu C. Aggarwal
2019 A* conf
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding.
KDD
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
2019 J jnl
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding.
CoRR
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
2019 J jnl
Temporal popularity prediction of locations for geographical placement of retail stores.
Knowl. Inf. Syst.
Hsun-Ping Hsieh, Fandel Lin, Cheng-Te Li, Ian En-Hsu Yen, Hsin-Yu Chen
2018 J jnl
D2KE: From Distance to Kernel and Embedding.
CoRR
Lingfei Wu, Ian En-Hsu Yen, Fangli Xu, Pradeep Ravikumar, Michael Witbrock
2018 J jnl
Learning Tensor Latent Features.
CoRR
Sung-En Chang, Xun Zheng, Ian En-Hsu Yen, Pradeep Ravikumar, Rose Yu
2018 A* conf
Loss Decomposition for Fast Learning in Large Output Spaces.
ICML
Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar
2018 A* conf
MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization.
NeurIPS
Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep Ravikumar, Shou-De Lin
2018 A conf
Random Warping Series: A Random Features Method for Time-Series Embedding.
AISTATS
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock
2018 J jnl
Random Warping Series: A Random Features Method for Time-Series Embedding.
CoRR
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, Michael Witbrock
2018 A* conf
Representer Point Selection for Explaining Deep Neural Networks.
NeurIPS
Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
2018 J jnl
Representer Point Selection for Explaining Deep Neural Networks.
CoRR
Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar
2018 J jnl
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability.
CoRR
Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
2018 A* conf
Scalable Spectral Clustering Using Random Binning Features.
KDD
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal
2018 J jnl
Scalable Spectral Clustering Using Random Binning Features.
CoRR
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen, Fangli Xu, Yinglong Xia, Charu C. Aggarwal
2018 — —
Sublinear-Time Learning and Inference for High-Dimensional Models.
Ian En-Hsu Yen
2018 A* conf
Word Mover's Embedding: From Word2Vec to Document Embedding.
EMNLP
Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
2018 J jnl
Word Mover's Embedding: From Word2Vec to Document Embedding.
CoRR
Lingfei Wu, Ian En-Hsu Yen, Kun Xu, Fangli Xu, Avinash Balakrishnan, Pin-Yu Chen, Pradeep Ravikumar, Michael J. Witbrock
2017 A* conf
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization.
ICML
Qi Lei, Ian En-Hsu Yen, Chao-Yuan Wu, Inderjit S. Dhillon, Pradeep Ravikumar
2017 A conf
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain.
AISTATS
Xiangru Huang, Ian En-Hsu Yen, Ruohan Zhang, Qixing Huang, Pradeep Ravikumar, Inderjit S. Dhillon
2017 A* conf
Latent Feature Lasso.
ICML
Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
2017 A* conf
PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification.
KDD
Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing
2017 A conf
Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.
AISTATS
Jiong Zhang, Ian En-Hsu Yen, Pradeep Ravikumar, Inderjit S. Dhillon
2016 A* conf
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery.
ICML
Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
2016 — conf
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain.
NIPS
Ian En-Hsu Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S. Dhillon
2016 A conf
Large-scale Submodular Greedy Exemplar Selection with Structured Similarity Matrices.
UAI
Dmitry Malioutov, Abhishek Kumar, Ian En-Hsu Yen
2016 A* conf
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification.
ICML
Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit S. Dhillon
2016 A* conf
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability.
KDD
Lingfei Wu, Ian En-Hsu Yen, Jie Chen, Rui Yan
2016 A conf
Scalable Exemplar Clustering and Facility Location via Augmented Block Coordinate Descent with Column Generation.
AISTATS
Ian En-Hsu Yen, Dmitry Malioutov, Abhishek Kumar
2015 A* conf
A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models.
ICML
Ian En-Hsu Yen, Xin Lin, Kai Zhong, Pradeep Ravikumar, Inderjit S. Dhillon
2015 — conf
A Dual Augmented Block Minimization Framework for Learning with Limited Memory.
NIPS
Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
2015 — conf
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent.
NIPS
Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2015 A* conf
Tackling the Achilles Heel of Social Networks: Influence Propagation based Language Model Smoothing.
WWW
Rui Yan, Ian En-Hsu Yen, Cheng-Te Li, Shiqi Zhao, Xiaohua Hu
2014 — conf
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings.
NIPS
Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon
2014 — conf
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators.
NIPS
Kai Zhong, Ian En-Hsu Yen, Inderjit S. Dhillon, Pradeep Ravikumar
2014 — conf
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space.
NIPS
Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep Ravikumar, Inderjit S. Dhillon
2013 A* conf
Indexed block coordinate descent for large-scale linear classification with limited memory.
KDD
Ian En-Hsu Yen, Chun-Fu Chang, Ting-Wei Lin, Shan-Wei Lin, Shou-De Lin
redb/extractors/enum.py
← Index redb/extractors/enum.py python 
from enum import Enum


class Tag(Enum):
    BASIC_PROPERTIES = "basic_properties"
    DIEC = "detect_it_easy"
    CAPA = "capa"
    MALCONTENT = "malcontent"
    DECOMPILED = "decompiled"
    HASH = "hash"
    STRINGS = "strings"
    RELATED = "relatedness" # unused
    PE_FEATURES = "pe_features"
    PE_IMPORT = "pe_import"
    PE_OVERLAY = "pe_overlay"
    PE_SECTION = "pe_section"
    PE_RESOURCE = "pe_resource"
    PE_SIGNATURE = "pe_signature"
    PE_CERTIFICATE = "pe_certificate"
    PE_SIGNER = "pe_signer"
    PE_DOTNET = "pe_dotnet"
    PE_EMBEDDED_EXTRAS = "pe_embedded_extras"
    PE_INCONSISTENCY_TESTS = "pe_inconsistency_tests"
    DOTNET_INCONSISTENCY_TESTS = "dotnet_inconsistency_tests"
    YARA = "yara"
    # Mach-O
    MACHO_FEATURES = "macho_features"
    MACHO_IMPORT = "macho_import"
    MACHO_EXPORT = "macho_export"
    MACHO_DYLIB = "macho_dylib"
    MACHO_SEGMENT = "macho_segment"
    MACHO_SIGNATURE = "macho_signature"
    MACHO_CERTIFICATE = "macho_certificate"
    MACHO_ENTITLEMENT = "macho_entitlement"
    MACHO_UNIVERSAL = "macho_universal"
    MACHO_HASHES = "macho_hashes"
    # ELF
    ELF_FEATURES = "elf_features"
    ELF_IMPORT = "elf_import"
    ELF_EXPORT = "elf_export"
    ELF_SECTION = "elf_section"
    ELF_SEGMENT = "elf_segment"
    ELF_SYMBOL = "elf_symbol"
    ELF_RELOCATION = "elf_relocation"
    ELF_NOTE = "elf_note"
    ELF_DEPENDENCY = "elf_dependency"
    # APK
    APK_FEATURES = "apk_features"
    APK_MANIFEST = "apk_manifest"
    APK_PERMISSIONS = "apk_permissions"
    APK_SIGNATURE = "apk_signature"
    APK_DEX = "apk_dex"
    APK_RESOURCES = "apk_resources"
    APK_NATIVE_LIBS = "apk_native_libs"
    APK_INCONSISTENCY_TESTS = "apk_inconsistency_tests"
    APK_DECOMPILED = "apk_decompiled"
    # JavaScript
    JS_FEATURES = "js_features"
    JS_SUSPICIOUS_APIS = "js_suspicious_apis"
    JS_STRINGS = "js_strings"
    JS_DEOBFUSCATION = "js_deobfuscation"
    JS_CONTENT = "js_content"